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This study develops an evolutionary strategy called DEPSO-GANN, which uses an artificial neural network (ANN) based on a parallel genetic algorithm (PGA) with migration for the adaptive control of integrated differential evolution (DE) and particle swarm optimization (PSO) to solve large-scale optimization problems, reduce calculation costs, and improve the stability of convergence towards the optimal...
A new strategy of Adaptive Plan System with Genetic Algorithm (APGA) is proposed to reduce a large amount of calculation cost and to improve stability in convergence to an optimal solution for multi-peak optimization problems with multi-dimensions. This is an approach that combines the global search ability of Genetic Algorithm (GA) and the local search ability of Adaptive Plan (AP). The APGA differs...
The expected explosion in traffic is driving the development of compact optical cross-connects and better spectrum utilization in fibers. Hierarchical optical path networks and elastic optical networks have been proposed independently to achieve these objectives. Due to their path-based characteristics, the role of network optimization techniques is critical in fully utilizing their benefits. In this...
We investigate the effect of path granularity and operation interval on dynamic path network operation with the aim of minimizing unused capacity and operation frequencies. We use various Internet traffic data sets to clarify the relations, and elucidate the general trends that enable us to effectively implement dynamic path control.
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